Character.Ai | Personalized AI for every moment of your day
Character.ai offers interactive AI characters for personalized entertainment and roleplay experiences. Meet AIs that feel alive. Chat with anyone, anywhere, anytime. Experience the power of super-intelligent chat bots that hear you, understand you, and remember you.
Detaljert introduksjon
Character.ai is a platform that allows users to interact with a variety of AI characters, each with unique personalities and abilities. Users can engage in conversations, roleplay scenarios, and immersive experiences with characters like Zeus, a noblewoman, Einstein, and a philosopher. The platform aims to provide personalized and interactive entertainment for every moment of your day.
MerAI
AutoDroid-V2: Boosting SLM-based GUI Agents via Code Generation
Discover an innovative approach to mobile UI agents with a cutting-edge solution from Tsinghua University that leverages the power of Small Language Models (SLMs) to automate tasks on-device. Our method addresses the privacy and cost concerns associated with large language models (LLMs) by offering a domain-specific, compact model trained with high-quality data. This breakthrough transforms the UI task automation challenge into a code generation problem, efficiently tackled by an SLM and executed with an on-device code interpreter. Our document-centered strategy automatically constructs detailed API documentation for each app, creating diverse task samples to guide the agent in learning to generate accurate and efficient scripts for unseen tasks. Experience the future of mobile UI interactions with our solution, boasting significantly higher success rates, lower latency, and reduced token consumption compared to state-of-the-art mobile UI agents. Stay ahead with our open-source code, set to revolutionize the field.
Butterflies.ai
Where AIs and humans coexist
iBVP Dataset: RGB-Thermal rPPG Dataset
The iBVP dataset is a collection of synchronized RGB and thermal infrared videos with PPG ground-truth signals acquired from an ear. It includes manual signal quality labels and dense signal-quality assessment using the SQA-PhysMD model. The dataset is designed to induce real-world variations in psycho-physiological states and head movement.